Sparse Representation for Target Detection in Hyperspectral Imagery
Sparse Representation for Target Detection in Hyperspectral Imagery
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DOI：
10.1109/jstsp.2011.2113170
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发表时间：
2011-06-01
影响因子：
7.5
通讯作者：
Tran, Trac D.
中科院分区：
文献类型：
--
作者：
Chen, Yi;Nasrabadi, Nasser M.;Tran, Trac D.
提出了一种基于稀疏性的高光谱图像目标自动检测算法。该算法基于HSI中的像素位于低维子空间的概念，因此可以表示为训练样本的稀疏线性组合。测试样本的稀疏表示（对应于几个选定的训练样本的线性组合的稀疏向量）可以通过求解l（0）范数最小化问题来恢复。随着压缩感知理论的发展，这种最小化问题可以转化为一个标准的线性规划问题，或者可以用贪婪追踪算法有效地近似。一旦得到稀疏向量，重构时就可以根据稀疏向量的特征来确定测试样本的类别。除了稀疏性和重建精度的约束，我们还利用了这样一个事实，即在HSI的相邻像素具有相似的光谱特性（平滑度）。在我们提出的算法中，平滑约束也施加了强制在每个重建像素的矢量拉普拉斯算子是最小的最小化过程中的所有时间。提出的基于稀疏性的算法被应用到几个高光谱图像检测感兴趣的目标。仿真结果表明，该算法优于经典的高光谱目标检测算法，如谱匹配滤波器、匹配子空间检测器、自适应子空间检测器以及支持向量机等二值分类器。
In this paper, we propose a new sparsity-based algorithm for automatic target detection in hyperspectral imagery (HSI). This algorithm is based on the concept that a pixel in HSI lies in a low-dimensional subspace and thus can be represented as a sparse linear combination of the training samples. The sparse representation (a sparse vector corresponding to the linear combination of a few selected training samples) of a test sample can be recovered by solving an l(0)-norm minimization problem. With the recent development of the compressed sensing theory, such minimization problem can be recast as a standard linear programming problem or efficiently approximated by greedy pursuit algorithms. Once the sparse vector is obtained, the class of the test sample can be determined by the characteristics of the sparse vector on reconstruction. In addition to the constraints on sparsity and reconstruction accuracy, we also exploit the fact that in HSI the neighboring pixels have a similar spectral characteristic (smoothness). In our proposed algorithm, a smoothness constraint is also imposed by forcing the vector Laplacian at each reconstructed pixel to be minimum all the time within the minimization process. The proposed sparsity-based algorithm is applied to several hyperspectral imagery to detect targets of interest. Simulation results show that our algorithm outperforms the classical hyperspectral target detection algorithms, such as the popular spectral matched filters, matched subspace detectors, adaptive subspace detectors, as well as binary classifiers such as support vector machines.